Change detection matters to a large number of organizations, such as municipalities and local governments, for a wide range of applications including map updating and hazard assessment. VHR satellite images have been increasingly used for urban change detection because they can provide adequate details of urban environment (Armenakis et al. 2010). Other remote sensing images, such as from aircraft and UAVs can also be used. Changes in urban areas for example can be detected by comparing corresponding pixels/objects in bi/multi-temporal remote sensing images, if the spatial relation between the corresponding pixels/objects is known. Hence, change detection is normally conducted in the following three steps:
1) establishing a spatial relation between bi-temporal images (coregistration),
2) specifying the element of change detection: object (through segmentation) or pixel,
3) indicating the change: analyzing the spectral or spatial features of the objects or pixels to find changes.
Among these steps, the role of coregistration is crucial, since any error in coregistration, i.e. misregistration, directly affects the accuracy of change detection. Misregistration may cause two types of errors in change detection: either error of omission, in which the changed object is classified as unchanged; or error of commission, in which the unchanged object is classified as changed (Sundaresan et al. 2007). In both cases, the change detection accuracy is decreased.
The coregistration task is even more serious in urban VHR imagery acquired with high off-nadir viewing angles due to severe relief displacements. Because of this effect, the tops of the elevated objects, e.g. buildings in urban areas, lean far from their footprints, exposing parts of their side exterior, i.e. building façade, and blocking other lower objects such as roads. The latter effect is called occlusion. This leaning can occur in different directions depending on the image viewing angles, so that the coregistration of corresponding pixels/objects in bi-temporal or multi-temporal images becomes extremely difficult.